The present invention relates to dynamic load control.
Controls have been developed for a variety of different load control processes. For example, in steam generation plants, several boilers and/or other types of steam generators generate steam and supply the steam to a common header. If individual pressure controllers with integral action running in parallel were used to control the boilers, instability in the operation of the boilers could result.
Therefore, pressure in the header is typically controlled by a single master pressure controller (MPC) that produces a total energy requirement (usually in terms of fuel feed) for the plant input. An energy allocation module divides the total energy requirement into separate fuel feed demands (i.e., set points) for the individual boilers.
The division of the total energy requirement implemented by the energy allocation module should be cost optimized according to varying economic conditions (such as price of fuels and electricity, environmental limits, etc.) and according to various constraints such as total production demand, technological constraints, runtime hours, and life time consumption. The underlying optimization problem is well known and has been solved in a variety of ways and with various levels of complexity since the 1960""s. For example, Real Time Optimizers have been implemented in order to optimize the cost of operating one or more loads.
These Real Time Optimizers have detected a steady state load requirement and then have provided control signals that optimize the cost of operating the loads based on this steady state load requirement. In order to operate in this fashion, the Real Time Optimizers have had to wait for transient process disturbances to settle out so that a steady state condition exists before such Optimizers can invoke their optimization procedures. However, for processes with slow dynamics and/or high levels of disturbances, the dependence of Real Time Optimizers on steady state information substantially deteriorates the performance of the control system, as no optimization is performed during the transients created by disturbances such as changes in set point and/or changes in load.
While predictive controllers have been used in the past, predictive controllers have not been used with Real Time Optimizers such that the Real Time Optimizers dynamically respond to target load values of the predicted process variables projected to the end of a prediction horizon. In one embodiment of the present invention, predictive controllers and Real Time Optimization are combined in this fashion in order to more effectively control loads during disturbances such as changes in set point and changes in load.
According to one aspect of the present invention, a process control system for controlling a load comprises a predictive controller and a real time optimizer. The predictive controller predicts an energy requirement for the load at prediction points k=0, 1, 2, . . . , K based on a steady state target energy requirement for the load. The real time optimizer determines an optimized dynamic energy demand requirement for the load at the prediction points k=0, 1, 2, . . . , K based on the predicted energy requirement, and controls the load based on the dynamic energy demand requirement.
According to another aspect of the present invention, a process control method for controlling loads 1, 2, . . . , N comprises the following: predicting, in a predictive controller, an energy requirement for each of the loads 1, 2, . . . , N at prediction points k=0, 1, 2, . . . , K based on a steady state target allocation for each of the loads 1, 2, . . . , N; determining, in a real time optimizer, a dynamic energy demand requirement for each of the loads 1, 2, . . . , N at prediction points k=0, 1, 2, . . . , K based on the predicted energy requirements; and, controlling the loads 1, 2, . . . , N based on the dynamic energy demand requirements.
According to yet another aspect of the present invention, a process control method for controlling loads 1, 2, . . . , N comprises the following: predicting, by way of a predictive controller, a total energy requirement for the loads 1, 2, . . . , N at prediction points k=0, 1, 2, . . . , K; allocating, by way of a real time optimizer, the total energy requirement to the loads 1, 2, . . . , N at the prediction points k=0, 1, 2, . . . , K based on a steady state target for each of the loads 1, 2, . . . , N; determining, by way of the real time optimizer, a dynamic energy demand requirement for each of the loads 1, 2, . . . , N at the prediction points k=0, 1, 2, . . . , K based on the allocated energy requirements; and, controlling the loads 1, 2, . . . , N based on the dynamic energy demand requirements.